Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations677
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.2 KiB
Average record size in memory88.0 B

Variable types

Numeric8
Categorical2

Alerts

Age is highly overall correlated with AgeCategory and 1 other fieldsHigh correlation
AgeCategory is highly overall correlated with Age and 1 other fieldsHigh correlation
Insulin is highly overall correlated with SkinThicknessHigh correlation
Pregnancies is highly overall correlated with Age and 1 other fieldsHigh correlation
SkinThickness is highly overall correlated with InsulinHigh correlation
Pregnancies has 92 (13.6%) zeros Zeros
SkinThickness has 183 (27.0%) zeros Zeros
Insulin has 323 (47.7%) zeros Zeros

Reproduction

Analysis started2024-11-09 14:33:24.132211
Analysis finished2024-11-09 14:33:45.469250
Duration21.34 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Pregnancies
Real number (ℝ)

High correlation  Zeros 

Distinct17
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8714919
Minimum0
Maximum17
Zeros92
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-11-09T14:33:45.657247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3736316
Coefficient of variation (CV)0.87140351
Kurtosis0.20127263
Mean3.8714919
Median Absolute Deviation (MAD)2
Skewness0.91557613
Sum2621
Variance11.38139
MonotonicityNot monotonic
2024-11-09T14:33:46.260304image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 123
18.2%
0 92
13.6%
2 90
13.3%
3 67
9.9%
4 61
9.0%
5 51
7.5%
6 45
 
6.6%
7 39
 
5.8%
8 31
 
4.6%
9 25
 
3.7%
Other values (7) 53
7.8%
ValueCountFrequency (%)
0 92
13.6%
1 123
18.2%
2 90
13.3%
3 67
9.9%
4 61
9.0%
5 51
7.5%
6 45
 
6.6%
7 39
 
5.8%
8 31
 
4.6%
9 25
 
3.7%
ValueCountFrequency (%)
17 1
 
0.1%
15 1
 
0.1%
14 2
 
0.3%
13 8
 
1.2%
12 9
 
1.3%
11 10
 
1.5%
10 22
3.2%
9 25
3.7%
8 31
4.6%
7 39
5.8%

Glucose
Real number (ℝ)

Distinct134
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.96455
Minimum0
Maximum199
Zeros5
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-11-09T14:33:47.001435image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile78
Q199
median114
Q3137
95-th percentile179
Maximum199
Range199
Interquartile range (IQR)38

Descriptive statistics

Standard deviation31.293352
Coefficient of variation (CV)0.26304771
Kurtosis0.92098212
Mean118.96455
Median Absolute Deviation (MAD)19
Skewness0.16841553
Sum80539
Variance979.27389
MonotonicityNot monotonic
2024-11-09T14:33:48.012075image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 16
 
2.4%
99 15
 
2.2%
106 14
 
2.1%
111 14
 
2.1%
108 13
 
1.9%
112 13
 
1.9%
95 13
 
1.9%
125 13
 
1.9%
122 12
 
1.8%
109 12
 
1.8%
Other values (124) 542
80.1%
ValueCountFrequency (%)
0 5
0.7%
44 1
 
0.1%
56 1
 
0.1%
57 1
 
0.1%
61 1
 
0.1%
62 1
 
0.1%
65 1
 
0.1%
67 1
 
0.1%
68 3
0.4%
71 4
0.6%
ValueCountFrequency (%)
199 1
 
0.1%
198 1
 
0.1%
197 1
 
0.1%
196 3
0.4%
195 2
0.3%
194 2
0.3%
193 1
 
0.1%
191 1
 
0.1%
190 1
 
0.1%
189 2
0.3%

BloodPressure
Real number (ℝ)

Distinct40
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.088626
Minimum38
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-11-09T14:33:48.471890image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile54
Q164
median72
Q380
95-th percentile90
Maximum106
Range68
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.396737
Coefficient of variation (CV)0.15809342
Kurtosis-0.029933566
Mean72.088626
Median Absolute Deviation (MAD)8
Skewness0.068647995
Sum48804
Variance129.88562
MonotonicityNot monotonic
2024-11-09T14:33:48.806374image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
70 51
 
7.5%
74 50
 
7.4%
72 44
 
6.5%
78 43
 
6.4%
68 43
 
6.4%
64 40
 
5.9%
76 37
 
5.5%
80 37
 
5.5%
60 34
 
5.0%
62 32
 
4.7%
Other values (30) 266
39.3%
ValueCountFrequency (%)
38 1
 
0.1%
40 1
 
0.1%
44 4
 
0.6%
46 1
 
0.1%
48 5
0.7%
50 11
1.6%
52 10
1.5%
54 11
1.6%
55 2
 
0.3%
56 12
1.8%
ValueCountFrequency (%)
106 3
 
0.4%
104 2
 
0.3%
102 1
 
0.1%
100 2
 
0.3%
98 2
 
0.3%
96 3
 
0.4%
95 1
 
0.1%
94 6
 
0.9%
92 8
1.2%
90 19
2.8%

SkinThickness
Real number (ℝ)

High correlation  Zeros 

Distinct48
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.939439
Minimum0
Maximum60
Zeros183
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-11-09T14:33:49.103325image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q332
95-th percentile43
Maximum60
Range60
Interquartile range (IQR)32

Descriptive statistics

Standard deviation15.276665
Coefficient of variation (CV)0.72956422
Kurtosis-1.1423698
Mean20.939439
Median Absolute Deviation (MAD)11
Skewness-0.10826787
Sum14176
Variance233.3765
MonotonicityNot monotonic
2024-11-09T14:33:49.451147image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 183
27.0%
32 31
 
4.6%
30 24
 
3.5%
27 22
 
3.2%
28 20
 
3.0%
18 19
 
2.8%
23 18
 
2.7%
31 18
 
2.7%
19 17
 
2.5%
39 17
 
2.5%
Other values (38) 308
45.5%
ValueCountFrequency (%)
0 183
27.0%
7 1
 
0.1%
8 2
 
0.3%
10 5
 
0.7%
11 6
 
0.9%
12 7
 
1.0%
13 10
 
1.5%
14 5
 
0.7%
15 14
 
2.1%
16 5
 
0.7%
ValueCountFrequency (%)
60 1
 
0.1%
54 2
 
0.3%
52 2
 
0.3%
51 1
 
0.1%
50 3
0.4%
49 2
 
0.3%
48 3
0.4%
47 4
0.6%
46 7
1.0%
45 5
0.7%

Insulin
Real number (ℝ)

High correlation  Zeros 

Distinct154
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.895126
Minimum0
Maximum325
Zeros323
Zeros (%)47.7%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-11-09T14:33:49.755321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median40
Q3120
95-th percentile230
Maximum325
Range325
Interquartile range (IQR)120

Descriptive statistics

Standard deviation82.216534
Coefficient of variation (CV)1.2109343
Kurtosis0.21609085
Mean67.895126
Median Absolute Deviation (MAD)40
Skewness1.0469727
Sum45965
Variance6759.5585
MonotonicityNot monotonic
2024-11-09T14:33:50.153660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 323
47.7%
105 11
 
1.6%
140 9
 
1.3%
120 8
 
1.2%
130 8
 
1.2%
180 7
 
1.0%
94 7
 
1.0%
135 6
 
0.9%
100 6
 
0.9%
110 6
 
0.9%
Other values (144) 286
42.2%
ValueCountFrequency (%)
0 323
47.7%
15 1
 
0.1%
16 1
 
0.1%
18 2
 
0.3%
22 1
 
0.1%
23 2
 
0.3%
29 1
 
0.1%
32 1
 
0.1%
36 3
 
0.4%
37 2
 
0.3%
ValueCountFrequency (%)
325 3
0.4%
321 1
 
0.1%
318 1
 
0.1%
310 1
 
0.1%
304 1
 
0.1%
300 1
 
0.1%
293 2
0.3%
291 1
 
0.1%
285 2
0.3%
284 1
 
0.1%

BMI
Real number (ℝ)

Distinct233
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.115657
Minimum18.2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-11-09T14:33:50.519521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum18.2
5-th percentile22.28
Q127.4
median32
Q336.3
95-th percentile43.52
Maximum50
Range31.8
Interquartile range (IQR)8.9

Descriptive statistics

Standard deviation6.4257519
Coefficient of variation (CV)0.20008159
Kurtosis-0.34146155
Mean32.115657
Median Absolute Deviation (MAD)4.5
Skewness0.26666499
Sum21742.3
Variance41.290287
MonotonicityNot monotonic
2024-11-09T14:33:50.969154image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 13
 
1.9%
31.6 12
 
1.8%
31.2 11
 
1.6%
33.3 10
 
1.5%
30.8 9
 
1.3%
32.8 9
 
1.3%
32.4 9
 
1.3%
33.6 8
 
1.2%
32.9 8
 
1.2%
34.2 8
 
1.2%
Other values (223) 580
85.7%
ValueCountFrequency (%)
18.2 3
0.4%
18.4 1
 
0.1%
19.1 1
 
0.1%
19.3 1
 
0.1%
19.4 1
 
0.1%
19.5 2
0.3%
19.6 1
 
0.1%
19.9 1
 
0.1%
20 1
 
0.1%
20.1 1
 
0.1%
ValueCountFrequency (%)
50 1
0.1%
49.7 1
0.1%
49.6 1
0.1%
49.3 1
0.1%
48.3 1
0.1%
47.9 2
0.3%
46.8 2
0.3%
46.7 1
0.1%
46.5 1
0.1%
46.3 1
0.1%

DiabetesPedigreeFunction
Real number (ℝ)

Distinct469
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46666765
Minimum0.078
Maximum2.288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-11-09T14:33:51.509967image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.1408
Q10.245
median0.371
Q30.614
95-th percentile1.1364
Maximum2.288
Range2.21
Interquartile range (IQR)0.369

Descriptive statistics

Standard deviation0.31562672
Coefficient of variation (CV)0.67634155
Kurtosis3.3831264
Mean0.46666765
Median Absolute Deviation (MAD)0.164
Skewness1.6017738
Sum315.934
Variance0.099620228
MonotonicityNot monotonic
2024-11-09T14:33:52.007583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.254 6
 
0.9%
0.258 5
 
0.7%
0.268 5
 
0.7%
0.259 5
 
0.7%
0.207 4
 
0.6%
0.299 4
 
0.6%
0.263 4
 
0.6%
0.26 4
 
0.6%
0.692 4
 
0.6%
0.261 4
 
0.6%
Other values (459) 632
93.4%
ValueCountFrequency (%)
0.078 1
0.1%
0.084 1
0.1%
0.085 2
0.3%
0.088 2
0.3%
0.089 1
0.1%
0.092 1
0.1%
0.096 1
0.1%
0.1 1
0.1%
0.101 1
0.1%
0.107 1
0.1%
ValueCountFrequency (%)
2.288 1
0.1%
1.893 1
0.1%
1.781 1
0.1%
1.699 1
0.1%
1.698 1
0.1%
1.6 1
0.1%
1.476 1
0.1%
1.461 1
0.1%
1.441 1
0.1%
1.4 1
0.1%

Age
Real number (ℝ)

High correlation 

Distinct46
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.868538
Minimum21
Maximum66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2024-11-09T14:33:52.626564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q340
95-th percentile56
Maximum66
Range45
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.091557
Coefficient of variation (CV)0.33745209
Kurtosis0.16679676
Mean32.868538
Median Absolute Deviation (MAD)7
Skewness0.99712901
Sum22252
Variance123.02263
MonotonicityNot monotonic
2024-11-09T14:33:53.575116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
22 66
 
9.7%
21 56
 
8.3%
24 42
 
6.2%
25 40
 
5.9%
23 32
 
4.7%
28 31
 
4.6%
27 30
 
4.4%
26 28
 
4.1%
29 26
 
3.8%
31 20
 
3.0%
Other values (36) 306
45.2%
ValueCountFrequency (%)
21 56
8.3%
22 66
9.7%
23 32
4.7%
24 42
6.2%
25 40
5.9%
26 28
4.1%
27 30
4.4%
28 31
4.6%
29 26
 
3.8%
30 18
 
2.7%
ValueCountFrequency (%)
66 4
0.6%
65 2
 
0.3%
64 1
 
0.1%
63 4
0.6%
62 3
0.4%
61 2
 
0.3%
60 3
0.4%
59 2
 
0.3%
58 7
1.0%
57 5
0.7%

Outcome
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
0
453 
1
224 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 453
66.9%
1 224
33.1%

Length

2024-11-09T14:33:53.846853image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T14:33:54.063382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 453
66.9%
1 224
33.1%

Most occurring characters

ValueCountFrequency (%)
0 453
66.9%
1 224
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 677
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 453
66.9%
1 224
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 677
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 453
66.9%
1 224
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 677
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 453
66.9%
1 224
33.1%

AgeCategory
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
1
441 
0
236 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 441
65.1%
0 236
34.9%

Length

2024-11-09T14:33:54.288328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T14:33:54.508240image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 441
65.1%
0 236
34.9%

Most occurring characters

ValueCountFrequency (%)
1 441
65.1%
0 236
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 677
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 441
65.1%
0 236
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 677
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 441
65.1%
0 236
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 677
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 441
65.1%
0 236
34.9%

Interactions

2024-11-09T14:33:42.054063image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:24.830886image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:27.080222image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:29.968437image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:33.892676image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:36.339048image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:38.165226image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:40.008366image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:42.280016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:25.301285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:27.396882image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:30.350760image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:34.125321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:36.572913image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:38.421307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:40.261756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:42.522134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:25.701729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:27.852688image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:30.732399image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:34.403347image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:36.819024image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:38.650712image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:40.501573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:42.854996image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:25.931112image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:28.240842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:32.497185image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:34.702001image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:37.043994image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:38.883119image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:40.721609image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:43.222820image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:26.175914image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:28.572088image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:32.794146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:35.167638image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:37.301665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:39.099624image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:41.220908image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:43.578917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:26.422143image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:28.911068image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:33.022065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:35.600627image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:37.516440image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:39.357865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:41.436891image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:43.918642image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:26.647792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:29.294484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:33.329995image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:35.898603image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:37.735172image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:39.584564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:41.643246image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:44.209916image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:26.857230image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:29.629074image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:33.612907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:36.113012image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:37.955677image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:39.793112image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T14:33:41.832752image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-09T14:33:54.691528image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
AgeAgeCategoryBMIBloodPressureDiabetesPedigreeFunctionGlucoseInsulinOutcomePregnanciesSkinThickness
Age1.0000.9940.1420.3820.0380.288-0.1120.3480.621-0.064
AgeCategory0.9941.0000.1420.3640.0200.2560.2250.2710.5310.272
BMI0.1420.1421.0000.3040.1400.1930.1690.2930.0160.445
BloodPressure0.3820.3640.3041.0000.0180.257-0.0760.1600.1860.056
DiabetesPedigreeFunction0.0380.0200.1400.0181.0000.0710.2240.247-0.0310.160
Glucose0.2880.2560.1930.2570.0711.0000.1680.4830.1480.019
Insulin-0.1120.2250.169-0.0760.2240.1681.0000.250-0.1380.502
Outcome0.3480.2710.2930.1600.2470.4830.2501.0000.2540.210
Pregnancies0.6210.5310.0160.186-0.0310.148-0.1380.2541.000-0.080
SkinThickness-0.0640.2720.4450.0560.1600.0190.5020.210-0.0801.000

Missing values

2024-11-09T14:33:44.626968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-09T14:33:45.277702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcomeAgeCategory
061487235033.60.6275011
11856629026.60.3513101
28183640023.30.6723211
318966239428.10.1672100
40137403516843.12.2883311
55116740025.60.2013001
637850328831.00.2482611
104110920037.60.1913001
1110168740038.00.5373411
1210139800027.11.4415701
PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcomeAgeCategory
7581106760037.50.1972601
7596190920035.50.2786611
76028858261628.40.7662200
76191707431044.00.4034311
762989620022.50.1423301
76310101764818032.90.1716301
76421227027036.80.3402701
7655121722311226.20.2453001
7661126600030.10.3494711
7671937031030.40.3152300